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An Approach to Analyzing Data From a Large Sub-Population of Single Cells

from Technology Networks

When analyzing gene expression profiles from large numbers of cells the average profile may not be a true representation of the many different profiles that could exist in the cell population (ex. in different states of growth, differentiation or activation). The transcriptional variability of individual cells and any insight into the relationship between specific genes gets lost. One aspect of this emerging field that still needs to be developed is data analysis. How to present data from single cell experiments? What are the proper controls and/or normalization methods to be used? Can you confidently identify sub-populations? The data analysis of high sample numbers with a reduced number of targets is not as straightforward as when using many targets with a few samples (i.e. arrays). Performing cluster analysis and displaying data as histograms can mask the identification of sub-populations. Using two model systems; CR25 (olig2-EGFP; a derivative of BG01) stem cell differentiation and EGF activation of CellSensor® AP1-bla ME-180 cells, a controlled set of data was collected from a large populations of cells. We will report on the tools used to analyze the gene expression profile of single cells within this larger population. For Research Use Only. Not intended for any animal or human therapeutic or diagnostic use.

about 9 years ago

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